Abstract
This study investigates the impact of dynamical downscaling on historical and future projections of winter extratropical cyclones over eastern North America and the western Atlantic Ocean. Six-hourly output from two global circulation models (GCMs), CCSM4 and GFDL-ESM2M, from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are used to create the initial and boundary conditions for 20 historical (1986–2005) and 20 future (2080–99) winter simulations using the Weather Research and Forecasting (WRF) Model. Two sets of WRF grid spacing (1.0° and 0.2°) are examined to determine the impact of model resolution. Although the cyclone frequency in the WRF runs is largely determined by the GCM predictions, the higher-resolution WRF reduces the underprediction in cyclone intensity. There is an increase in late-twenty-first-century cyclone activity over the east coast of North America in CCSM4 and its WRF, whereas there is little change in GFDL-ESM2M and WRF given that there is a larger decrease in the temperature gradient in this region. There is a future increase in relatively deep cyclones over the East Coast in the high-resolution WRF forced by CCSM4. These storms are weaker than the historical cases early in their life cycle, but then because of latent heating they rapidly develop and become stronger than the historical events. This increase does not occur in the low-resolution WRF or the high-resolution WRF forced by GFDL since the latent heat increase is relatively small. This implies that the diabatic processes during cyclogenesis may become more important in a warmer climate, and these processes may be too weak in existing coarse-resolution GCMs.
© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
Publisher’s Note: This article was revised on 18 December 2018 to include the designation that it belongs to the Process-Oriented Model Diagnostics special collection.
This article is included in the Process-Oriented Model Diagnostics Special Collection.
